DC-7: Bilge Tuna
Contact
E-mail: btuna26@ku.edu.tr
Project
Machine Learning-Based Safety Assessment of Water Electrolysers for Green Hydrogen Production
Host Organization
KU
Supervisors
Prof. Can Erkey (Main, KU); Prof. Gürkan Sin (co-supervisor, DTU)
Objectives
- Develop dynamic models for safety assessment of water electrolysers under fluctuating renewable energy inputs.
- Investigate hydrogen crossover mechanisms and their impact on operational safety.
- Apply machine learning techniques to predict safety-critical events and improve process reliability.
- Develop intelligent monitoring and decision-support tools for safe and sustainable green hydrogen production.
Project Description
Green hydrogen is expected to play an important role in the transition to sustainable energy systems, and anion exchange membrane (AEM) water electrolysers are emerging as a promising technology for its production. However, operating AEM electrolysers with renewable energy sources such as wind and solar can lead to rapidly changing operating conditions, which may affect both system performance and safety. Understanding these effects is important for the reliable and large-scale deployment of hydrogen technologies.
This doctoral project will focus on the safety assessment of AEM electrolysers operating under dynamic conditions. The main objective is to better understand how changing operating parameters influence system behaviour and safety-related phenomena, particularly hydrogen crossover through the membrane. The project will investigate the effects of factors such as current density, temperature, and renewable power fluctuations on electrolyser operation.
To achieve this, physics-based models will be developed to describe the main electrochemical and transport processes in the system. In parallel, machine learning methods will be explored to analyse process data and identify patterns that may indicate unsafe operating conditions. Combining these approaches may improve the prediction of critical events and support the development of more reliable monitoring strategies.
The project will also examine how modelling and data-driven methods can be used together to support safer and more efficient operation of AEM electrolyser systems. The outcomes are expected to contribute to the understanding of electrolyser safety and to provide useful tools for the future development of intelligent monitoring and control systems for green hydrogen technologies.
Relevant Background
- B.Sc. in Chemical Engineering with a strong background in process engineering, transport phenomena, and mathematical modelling.
- M.Sc. in Energy Management, with a master's thesis entitled "Techno-Economic Assessment and Proposed Model Development for Green Hydrogen Production in Ireland", focusing on renewable energy integration and hydrogen production systems.
- Research experience in techno-economic analysis and optimisation of green hydrogen production integrated with wind energy.
Publications